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Drying behaviour of apple particles was investigated in a laboratory type dryer. The effect of drying air temperature, airflow velocity, initial height of layer, particles shape and size on the dehydration characteristics of apples was investigated. Increase in drying air temperature and increase in the airflow velocity caused a decrease in the drying time and an increase in drying rate. Increase in initial height of layer and increase in the sample thickness caused an increase in the drying time and decrease in drying rate. Drying time of the cubes was shorter and their drying rate was higher than for slices. The experimental dehydration data of apple particles obtained were fitted to the semi-theoretical, empirical and theoretical models. The accuracies of the models were measured using the correlation coefficient (R), mean bias error (MBE), root mean square error (RMSE), reduced chi-square (v 2 ), and t-statistic method. All models described the drying characteristics of apple particles satisfactorily (R > 0.9792). The Logarythmic model can be considered as the most appropriate (R > 0.9976, MBE = )10 )11 )4.5 · 10 )6 , RMSE = 0.00287-0.01746, v 2 = 8.5 · 10 )6 )3.1 · 10 )4 , t-stat = 7.3 · 10 )9 )1.2 · 10 )3 ). The effect of drying air temperature, airflow velocity, characteristic dimension of the particle and initial height of layer on the drying models parameters were also determined.

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In this study, an efficient optimisation method by combining response surface methodology (RSM) and genetic algorithm (GA) is introduced to find the optimal topology of artificial neural networks (ANNs) for predicting colour changes in rehydrated apple cubes. A multi-layered feed-forward backpropagation ANN model of algorithms was developed to correlate one output (colour change) to four input variables (drying air temperature, drying air velocity, temperature of distilled water and rehydration time). A predictive model for ANN topology in terms of the best mean squared error (MSE) performance on validation samples was created using RSM. RSM model was integrated with an effective GA to find the optimum topology of ANN. The optimum ANN had minimum MSE when the number of hidden neurons, learning rate, momentum constant, number of epochs and number of training runs were 13, 0.33, 0.89, 3869 and 3, respectively. MSE of optimal ANN topology on validation samples was 0.0072095. It turned out that the optimal ANN topology can be considered as more precise for predicting colour change in the rehydrated apple cubes. Mean absolute error and regression coefficient (R) of the optimal ANN topology were determined as 0.0259 and 0.96475 for training, 0.0399 and 0.95243 for testing and 0.0264 and 0.95151 for validation data sets. The results of the testing model on new samples showed excellent agreement between the actual and predicted data with coefficient of determination R 2 = 0.97.

The wood biomass obtained from four tree species: spruce, beech, willow and alder was studied. Sawdust obtained by sawing of wood was dried in a convective dryer at 80°C, without drying-agent flow. The measurements taken with oven-drying method covered determination of initial moisture and its detailed changes during entire drying process. Both DSC curves and spectral data from infrared spectra registered in the classic MIR range were also used to monitor change in water content in studied samples within entire drying process. Results obtained suggest that the DSC curves can be efficiently used as a discriminant to distinguish dry and wet biomass samples, as significant differences within courses of specific DSC curves of woody biomass before and after drying process occurred. Calorimetrically measured values of enthalpy of evaporation of water and water content in studied biomass samples were directly proportional to high determination coefficient, i.e. 0.9653. Statistical analysis proved quantitative directly proportional correlation between content of water measured with oven-drying method and IR spectral data of studied samples.

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